Plant Genome Informatics: Evaluation and Analysis of Genomic DNA Features Involved in the Transcriptional Processing of Protein
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University of Maryland Establishes Center for Health Related Informatics and Bioimaging
University of Maryland Establishes Center for Health Related Informatics and Bioimaging News & Events UNIVERSITY OF MARYLAND ESTABLISHES CENTER FOR HEALTH CONTACT RELATED INFORMATICS AND BIOIMAGING Monday, June 10, 2013 Karen Robinson, Media Relations New Center, Led By Distinguished Bioinformatics Scientist Owen White, (410) 706-7590 Ph.D., Unites Scientists Across Disciplines to Integrate Genomics and Personalized Medicine into Research and Clinical Care [email protected] University of Maryland, Baltimore campus President Jay A. Perman, M.D., and Media Relations Team University of Maryland School of Medicine Dean E. Albert Reece, M.D., Ph.D., M.B.A., wish to announce the establishment of a new center to unite research scientists and physicians across disciplines. The center will employ these SEARCH ARTICLES interdisciplinary connections to enhance the use of cutting edge medical science such as genomics and personalized medicine to accelerate research discoveries and improve health care outcomes. Participants in the new News Archives University of Maryland Center for Health-Related Informatics and Bioimaging (CHIB) will collaborate with computer scientists, engineers, life scientists and others at a similar center at the University of Maryland, College Park campus, CHIB Co-Director Owen White, Ph.D. together forming a joint center supported by the M-Power Maryland initiative. LEARN MORE University of Maryland School of Medicine Dean E. Albert Reece, M.D., Ph.D., M.B.A., with the concurrence of President Perman, has appointed as co-director of the new center Owen White, Ph.D., Professor of Epidemiology and Public Health and Director of Bioinformatics at the University of Maryland School of Medicine Institute for Genome Sciences. -
Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers
RESEARCH ARTICLE Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers Salma Jamal1☯‡, Sonam Arora2☯‡, Vinod Scaria3* 1 CSIR Open Source Drug Discovery Unit (CSIR-OSDD), Anusandhan Bhawan, Delhi, India, 2 Delhi Technological University, Delhi, India, 3 GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India ☯ These authors contributed equally to this work. ‡ These authors are joint first authors on this work. * [email protected] a11111 Abstract Background The dynamic and differential regulation and expression of genes is majorly governed by the OPEN ACCESS complex interactions of a subset of biomolecules in the cell operating at multiple levels start- Citation: Jamal S, Arora S, Scaria V (2016) ing from genome organisation to protein post-translational regulation. The regulatory layer Computational Analysis and Predictive contributed by the epigenetic layer has been one of the favourite areas of interest recently. Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers. PLoS ONE 11(9): This layer of regulation as we know today largely comprises of DNA modifications, histone e0083032. doi:10.1371/journal.pone.0083032 modifications and noncoding RNA regulation and the interplay between each of these major Editor: Wei Yan, University of Nevada School of components. Epigenetic regulation has been recently shown to be central to development Medicine, UNITED STATES of a number of disease processes. The availability of datasets of high-throughput screens Received: June 10, 2013 for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries. -
Genome Informatics 2016 Davide Chicco1 and Michael M
Chicco and Hoffman Genome Biology (2017) 18:5 DOI 10.1186/s13059-016-1135-5 MEETINGREPORT Open Access Genome Informatics 2016 Davide Chicco1 and Michael M. Hoffman1,2,3* Abstract of sequence variants. Konrad Karczewski (Massachusetts General Hospital, USA) presented the Loss Of Func- A report on the Genome Informatics conference, held tion Transcript Effect Estimator (LOFTEE, https://github. at the Wellcome Genome Campus Conference Centre, com/konradjk/loftee). LOFTEE uses a support vector Hinxton, United Kingdom, 19–22 September 2016. machine to identify sequence variants that significantly disrupt a gene and potentially affect biological processes. We report a sampling of the advances in computational Martin Kircher (University of Washington, USA) dis- genomics presented at the most recent Genome Informat- cussed a massively parallel reporter assay (MPRA) that ics conference. As in Genome Informatics 2014 [1], speak- uses a lentivirus for genomic integration, called lentiM- ers presented research on personal and medical genomics, PRA [3]. He used lentiMPRA to predict enhancer activity, transcriptomics, epigenomics, and metagenomics, new and to more generally measure the functional effect of sequencing techniques, and new computational algo- non-coding variants. William McLaren (European Bioin- rithms to crunch ever-larger genomic datasets. Two formatics Institute, UK) presented Haplosaurus, a variant changes were notable. First, there was a marked increase effect predictor that uses haplotype-phased data (https:// in the number of projects involving single-cell analy- github.com/willmclaren/ensembl-vep). ses, especially single-cell RNA-seq (scRNA-seq). Second, Two presenters discussed genome informatics while participants continued the practice of present- approaches to the analysis of cancer immunotherapy ing unpublished results, a large number of the presen- response. -
Comparative Genomics of Arabidopsis and Maize: Prospects and Comment Limitations Volker Brendel*, Stefan Kurtz† and Virginia Walbot‡
http://genomebiology.com/2002/3/3/reviews/1005.1 Minireview Comparative genomics of Arabidopsis and maize: prospects and comment limitations Volker Brendel*, Stefan Kurtz† and Virginia Walbot‡ Addresses: *Department of Zoology and Genetics and Department of Statistics, Iowa State University, Ames, IA 50010, USA. †Technische Fakultät, Universität Bielefeld, D-33501 Bielefeld, Germany. ‡Department of Biological Sciences, Stanford University, Stanford, CA 94305- 5020, USA. Correspondence: Volker Brendel. E-mail: [email protected] reviews Published: 14 February 2002 Genome Biology 2002, 3(3):reviews1005.1–1005.6 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2002/3/3/reviews/1005 © BioMed Central Ltd (Print ISSN 1465-6906; Online ISSN 1465-6914) reports Abstract The completed Arabidopsis genome seems to be of limited value as a model for maize genomics. In addition to the expansion of repetitive sequences in maize and the lack of genomic micro-colinearity, maize-specific or highly-diverged proteins contribute to a predicted maize proteome of about 50,000 deposited research proteins, twice the size of that of Arabidopsis. Maize (Zea mays L., corn) was domesticated in the high- contributions to agriculture through the discovery of hybrid lands of Central Mexico approximately 10,000 years ago [1]. vigor and cytoplasmic male sterility. Corn agriculture spread rapidly into diverse climate zones, ranging from 45° N to 45° S, and supported vast Native The beautiful detail evident in meiotic maize chromosomes refereed research American civilizations. Today, maize is one of the world’s stimulated a generation of gifted cytogeneticists to identify most important crops: for direct human consumption, as a the physical basis for recombination, to construct linkage key component of animal feed, and as the source of chemical maps tied to chromosomes, and to analyze the consequences feed stocks. -
Teaching Bioinformatics and Neuroinformatics Using Free Web-Based Tools
UCLA Behavioral Neuroscience Title Teaching Bioinformatics and Neuroinformatics Using Free Web-based Tools Permalink https://escholarship.org/uc/item/2689x2hk Author Grisham, William Publication Date 2009 Supplemental Material https://escholarship.org/uc/item/2689x2hk#supplemental Peer reviewed eScholarship.org Powered by the California Digital Library University of California – SUBMITTED – [Article; 34,852 Characters] Teaching Bioinformatics and Neuroinformatics Using Free Web-based Tools William Grisham 1, Natalie A. Schottler 1, Joanne Valli-Marill 2, Lisa Beck 3, Jackson Beatty 1 1Department of Psychology, UCLA; 2 Office of Instructional Development, UCLA; 3Department of Psychology, Bryn Mawr College Keywords: quantitative trait locus, digital teaching tools, web-based learning, genetic analysis, in silico tools DRAFT: Copyright William Grisham, 2009 Address correspondence to: William Grisham Department of Psychology, UCLA PO Box 951563 Los Angeles, CA 90095-1563 [email protected] Grisham Teaching Bioinformatics Using Web-Based Tools ABSTRACT This completely computer-based module’s purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. This module integrates information gathered from websites dealing with anatomy (Mouse Brain Library), Quantitative Trait Locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, NCBI Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). This module provides for teaching genetics from the phenotypic level to the molecular level, some neuroanatomy, some aspects of histology, statistics, Quantitaive Trait Locus analysis, molecular biology including in situ hybridization and microarray analysis in addition to introducing bioinformatic resources. -
Gene Selection Using Gene Expression Data in a Virtual Environment
276 Genome Informatics 13: 276-277 (2002) Gene Selection Using Gene Expression Data in a Virtual Environment Kunihiro Nishimura1 Shumpei Ishikawa2 [email protected] [email protected] Shuichi Tsutsumi2 Hiroyuki Aburatani2 [email protected] [email protected] Koichi Hirota2 Michitaka Hirose2 [email protected] [email protected] 1 Graduate School of Information Science and Technology , The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan 2 Reseach Center for Advanced Science and Technology , The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan Keywords: gene selection, gene expression, virtual reality, visualization 1 Gene Selection for Drug Discovery The gene expression data of many normal and diseased tissues enables the selection of genes that satisfy the conditions for finding a new drug. In the process of selecting genes is required to determine many filtering parameters. The system of the selection of genes from all genes is required to have the following features: 1) visualization of the data to provide the users to grasp both holistic and detail views of the data, 2) interactivity to determine many filtering parameters, 3) provides spatial cue to understand intuitively because there are many filtering parameters. In order to visualize various information of large number of genes, the system also needs a wide field of view. Virtual reality technology satisfies these requirements above. 2 Virtual Reality Virtual reality technology can help improve genome data analysis by enabling interactive visualization and providing a three-dimensional virtual environment. -
Cheminformatics Education
Cheminformatics Education Introduction Despite some skepticism at the turn of the century1,2 the terms “cheminformatics” and “chemoinformatics” are now in common parlance. The term “chemical informatics” is used less. The premier journal in the field, the Journal of Chemical Information and Modeling, does not use any of these terms and its cheminformatics papers are scattered across multiple sections including “chemical Information”. Unfortunately it is not just the name of the discipline that is undefined: opinions also vary on the scope. Paris supplied the following definition1 (shortened here): “Chem(o)informatics is a generic term that encompasses the design, creation, organization, storage, management, retrieval, analysis, dissemination, visualization and use of chemical information, not only in its own right, but as a surrogate or index for other data, information and knowledge”. Brown3 says the discipline is “mixing of information resources to transform data into information, and information into knowledge, for the intended purpose of making better decisions faster in the arena of drug lead identification and optimization”. Gasteiger4 says that cheminformatics is the application of informatics methods to solve chemical problems and Bajorath5 agrees that a broad definition such as that is needed to cover all the different scientific activities that have evolved, or have been assimilated, under the cheminformatics umbrella. Varnek’s definition6,7 is rather different. He considers that cheminformatics is a part of theoretical chemistry based on its own molecular model; unlike quantum chemistry in which molecules are represented as ensemble of electrons and nuclei, or force fields molecular modeling dealing with classical “atoms” and “bonds”, cheminformatics considers molecules as objects (graphs and vectors) in multidimensional chemical space. -
Informatics and the Human Genome Project
Invited manuscript: submitted to IEEE Engineering in Medicine and Biology for a special issue on genome informatics. INFORMATICS AND THE HUMAN GENOME PROJECT Robert J. Robbins US Department of Energy [email protected] David Benton National Center for Human Genome Research [email protected] Jay Snoddy US Department of Energy [email protected] TABLE OF CONTENTS INTRODUCTION 1 INFORMATION AND GENOME PROJECTS 3 THE NATURE OF INFORMATION TECHNOLOGY 4 MOORE’S LAW 5 INFORMATICS 6 INFORMATICS ENABLES BIG SCIENCE 6 THE INTELLECTUAL STANDING OF INFORMATICS 7 AGENCY COMMITMENTS 8 COMMUNITY RECOMMENDATIONS 8 OTA Report 9 Baltimore White Paper 9 GeSTeC Report 10 Workshop on Database Interoperability 11 Meeting on Interconnection of Molecular Biology Databases (MIMBD) 11 Interoperable Software Tools 12 SUMMARY NEEDS 12 CURRENT TRENDS 12 FUTURE SUPPORT 13 OPEN ISSUES 13 Invited manuscript: submitted to IEEE Engineering in Medicine and Biology for a special issue on genome informatics. INFORMATICS AND THE HUMAN GENOME PROJECT1 ROBERT J. ROBBINS, DAVID BENTON, AND JAY SNODDY INTRODUCTION Information technology is transforming biology and the relentless effects of Moore’s Law (discussed later) are transforming that transformation. Nowhere is this more apparent than in the international collaboration known as the Human Genome Project (HGP). Before considering the relationship of informatics to genomic research, let us take a moment to consider the science of the HGP. It has been known since antiquity that like begets like, more or less. Cats have kittens, dogs have puppies, and acorns grow into oak trees. A scientific basis for that observation was first provided with the development of the new science of genetics at the beginning of this century. -
Introduction to Bioinformatics a Complex Systems Approach
Introduction to Bioinformatics A Complex Systems Approach Luis M. Rocha Complex Systems Modeling CCS3 - Modeling, Algorithms, and Informatics Los Alamos National Laboratory, MS B256 Los Alamos, NM 87545 [email protected] or [email protected] 1 Bioinformatics: A Complex Systems Approach Course Layout 3 Monday: Overview and Background Luis Rocha 3 Tuesday: Gene Expression Arrays – Biology and Databases Tom Brettin 3 Wednesday: Data Mining and Machine Learning Luis Rocha and Deborah Stungis Rocha 3 Thursday: Gene Network Inference Patrik D'haeseleer 3 Friday: Database Technology, Information Retrieval and Distributed Knowledge Systems Luis Rocha 2 Bioinformatics: A Complex Systems Approach Overview and Background 3A Synthetic Approach to Biology Information Processes in Biology – Biosemiotics Genome, DNA, RNA, Protein, and Proteome – Information and Semiotics of the Genetic System Complexity of Real Information Proceses – RNA Editing and Post-Transcription changes Reductionism, Synthesis and Grand Challenges Technology of Post-genome informatics – Sequence Analysis: dynamic programming, simulated anealing, genetic algorithms Artificial Life 3 Information Processes in Biology Distinguishes Life from Non-Life Different Information Processing Systems (memory) 3 Genetic System Construction (expression, development, and maintenance) of cells ontogenetically: horizontal transmission Heredity (reproduction) of cells and phenotypes: vertical transmission 3 Immune System Internal response based on accumulated experience (information) 3 -
Bioinformatics: a Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D
BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto BIOINFORMATICS SECOND EDITION METHODS OF BIOCHEMICAL ANALYSIS Volume 43 BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ᭧ 2001 by John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. -
Quality Assessment of Maize Assembled Genomic Islands (Magis) and Large-Scale Experimental Verification of Predicted Genes
Quality assessment of maize assembled genomic islands (MAGIs) and large-scale experimental verification of predicted genes Yan Fu*†‡, Scott J. Emrich‡§¶, Ling Guo†§, Tsui-Jung Wenʈ, Daniel A. Ashlock§**††‡‡, Srinivas Aluru§¶**§§, and Patrick S. Schnable*†§ʈ**§§¶¶ *Interdepartmental Genetics Graduate Program, §Interdepartmental Bioinformatics and Computational Biology Graduate Program, **L. H. Baker Center for Bioinformatics and Biological Statistics, §§Center for Plant Genomics, and Departments of †Genetics, Development, and Cell Biology, ¶Electrical and Computer Engineering, ʈAgronomy, and ††Mathematics, Iowa State University, Ames, IA 50011 Edited by Susan R. Wessler, University of Georgia, Athens, GA, and approved July 5, 2005 (received for review April 26, 2005) Recent sequencing efforts have targeted the gene-rich regions of ically, this speed makes it possible to determine the effects of the maize (Zea mays L.) genome. We report the release of an different assembly parameter values on the quality of the improved assembly of maize assembled genomic islands (MAGIs). resulting assemblies. The 114,173 resulting contigs have been subjected to computa- Three research groups currently provide publicly available partial tional and physical quality assessments. Comparisons to the se- maize genome assemblies based on the GSS data [The Institute for quences of maize bacterial artificial chromosomes suggest that at Genomic Research (TIGR), Plant Genome Database, and our least 97% (160 of 165) of MAGIs are correctly assembled. Because group]. -
An Active DNA Transposon Family in Rice
letters to nature reference to their impact on the salt marsh. Contrib. Mar. Sci. 23, 25–55 (1980). transposons comprise the largest component of transposable 8. Zeil, J. & Layne, J. Crustacean Experimental Systems in Neurobiology (ed. Wiese, K.) 227–247 (Springer, Heidelberg, 2002). elements in the rice genome (14% of the genomic DNA) but, 9. Zeil, J., Nalbach, G. & Nalbach, H.-O. Eyes, eye stalks, and the visual world of semi-terrestrial crabs. numerically, MITEs form the largest group with over 100,000 J. Comp. Physiol. A 159, 801–811 (1986). elements divided into hundreds of families comprising about 6% 10. Krapp, H. G., Hengstenberg, B. & Hengstenberg, R. Dendritic structure and receptive-field of the genome6,7. MITEs are the predominant transposable element organization of optic flow processing interneurons in the fly. J. Neurophysiol. 79, 1902–1917 (1998). 11. Wehner, R. ‘Matched filters’—neural models of the external world. J. Comp. Physiol. A 161, 511–531 associated with the non-coding regions of the genes of flowering (1987). plants, especially grasses, and have been found in several animal 12. Schall, R. Estimation in generalized linear models with random effects. Biometrika 78, 719–727 genomes including Caenorhabditis elegans, mosquitoes, fish and (1991). human (reviewed in ref. 8). 13. Zeil, J. & Al-Mutairi, M. M. The variation of resolution and of ommatidial dimensions in the eyes of the fiddler crab Uca lactea annulipes (Ocypodidae, Brachyura, Decapoda). J. Exp. Biol. 199, 1569–1577 Structurally, MITEs are reminiscent of non-autonomous DNA (1996). (class 2) elements with their small size (,600 base pairs) and short 14.